(A)kNN Query Processing on the Cloud: A Survey

被引:4
|
作者
Nodarakis, Nikolaos [1 ]
Rapti, Angeliki [1 ]
Sioutas, Spyros [2 ]
Tsakalidis, Athanasios K. [1 ]
Tsolis, Dimitrios [3 ]
Tzimas, Giannis [4 ]
Panagis, Yannis [5 ]
机构
[1] Univ Patras, Comp Engn & Informat Dept, Patras 26504, Greece
[2] Ionian Univ, Dept Informat, Corfu 49100, Greece
[3] Univ Patras, Dept Cultural Heritage Management & New Technol, Patras 26504, Greece
[4] Inst Western Greece, Comp & Informat Engn Dept, Technol Educ, Patras 26334, Greece
[5] Univ Copenhagen, Ctr Excellence Int Courts, DK-1455 Copenhagen, Denmark
来源
ALGORITHMIC ASPECTS OF CLOUD COMPUTING, ALGOCLOUD 2016 | 2017年 / 10230卷
关键词
Big data; Nearest neighbor; MapReduce; NoSQL; Query processing; SPATIAL DATA; NEIGHBOR; SYSTEM; HADOOP;
D O I
10.1007/978-3-319-57045-7_3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. Although (A) kNN is a fundamental query type, it is computationally very expensive. During the last years a multiplicity of research papers has focused around the distributed (A) kNN query processing on the cloud. This work constitutes a survey of research efforts towards this direction. The main contribution of this work is an up-to-date review of the latest (A) kNN query processing approaches. Finally, we discuss various research challenges and directions of further research around this domain.
引用
收藏
页码:26 / 40
页数:15
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